Overview

Dataset statistics

Number of variables20
Number of observations648322
Missing cells309786
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory98.9 MiB
Average record size in memory160.0 B

Variable types

Categorical8
Numeric12

Alerts

reportingmunicipalityid has constant value "1061" Constant
egid has a high cardinality: 7701 distinct values High cardinality
statyear is highly correlated with personpseudoid and 1 other fieldsHigh correlation
personpseudoid is highly correlated with statyear and 1 other fieldsHigh correlation
ageclass is highly correlated with maritalstatusclass and 2 other fieldsHigh correlation
maritalstatusclass is highly correlated with ageclassHigh correlation
arrivalyearmunicipality is highly correlated with ageclass and 1 other fieldsHigh correlation
gastws is highly correlated with gkats and 3 other fieldsHigh correlation
gazwot is highly correlated with gastws and 2 other fieldsHigh correlation
ewid is highly correlated with gastws and 2 other fieldsHigh correlation
householdid is highly correlated with statyear and 1 other fieldsHigh correlation
arrivalyearswitzerland is highly correlated with ageclass and 2 other fieldsHigh correlation
wareaclass is highly correlated with wazimclassHigh correlation
gkats is highly correlated with gastws and 1 other fieldsHigh correlation
wazimclass is highly correlated with gkats and 1 other fieldsHigh correlation
reportingmunicipalityid is highly correlated with wareaclass and 5 other fieldsHigh correlation
nationalityclass is highly correlated with arrivalyearswitzerlandHigh correlation
populationtype is highly correlated with reportingmunicipalityidHigh correlation
gbaups is highly correlated with nhHigh correlation
eh is highly correlated with nhHigh correlation
nh is highly correlated with gbaups and 4 other fieldsHigh correlation
arrivalyearswitzerland has 309786 (47.8%) missing values Missing
personpseudoid has unique values Unique
ageclass has 6689 (1.0%) zeros Zeros

Reproduction

Analysis started2022-09-29 07:23:24.386812
Analysis finished2022-09-29 07:24:30.807671
Duration1 minute and 6.42 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

reportingmunicipalityid
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
1061
648322 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2593288
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1061
2nd row1061
3rd row1061
4th row1061
5th row1061

Common Values

ValueCountFrequency (%)
1061648322
100.0%

Length

2022-09-29T09:24:30.874873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:24:30.969617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1061648322
100.0%

Most occurring characters

ValueCountFrequency (%)
11296644
50.0%
0648322
25.0%
6648322
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2593288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11296644
50.0%
0648322
25.0%
6648322
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common2593288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11296644
50.0%
0648322
25.0%
6648322
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2593288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11296644
50.0%
0648322
25.0%
6648322
25.0%

statyear
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.394782
Minimum2012
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:31.037398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12015
median2017
Q32019
95-th percentile2020
Maximum2020
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.491499883
Coefficient of variation (CV)0.001235621072
Kurtosis-0.9710227481
Mean2016.394782
Median Absolute Deviation (MAD)2
Skewness-0.2411116064
Sum1307273098
Variance6.207571665
MonotonicityIncreasing
2022-09-29T09:24:31.122052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
202082090
12.7%
201981588
12.6%
201581329
12.5%
201481111
12.5%
201881072
12.5%
201681023
12.5%
201780933
12.5%
201279176
12.2%
ValueCountFrequency (%)
201279176
12.2%
201481111
12.5%
201581329
12.5%
201681023
12.5%
201780933
12.5%
201881072
12.5%
201981588
12.6%
202082090
12.7%
ValueCountFrequency (%)
202082090
12.7%
201981588
12.6%
201881072
12.5%
201780933
12.5%
201681023
12.5%
201581329
12.5%
201481111
12.5%
201279176
12.2%

personpseudoid
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct648322
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.016794925 × 1014
Minimum2.012400076 × 1014
Maximum2.020400135 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:31.236649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.012400076 × 1014
5-th percentile2.012400111 × 1014
Q12.01540017 × 1014
median2.017400031 × 1014
Q32.019400144 × 1014
95-th percentile2.020400095 × 1014
Maximum2.020400135 × 1014
Range8.000059248 × 1011
Interquartile range (IQR)3.99997441 × 1011

Descriptive statistics

Standard deviation2.491481859 × 1011
Coefficient of variation (CV)0.001235366982
Kurtosis-0.9709787101
Mean2.016794925 × 1014
Median Absolute Deviation (MAD)1.999862345 × 1011
Skewness-0.2411306764
Sum1.626043436 × 1018
Variance6.207481855 × 1022
MonotonicityNot monotonic
2022-09-29T09:24:31.371130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.012400153 × 10141
 
< 0.1%
2.01840005 × 10141
 
< 0.1%
2.018400083 × 10141
 
< 0.1%
2.018400071 × 10141
 
< 0.1%
2.018400109 × 10141
 
< 0.1%
2.018400136 × 10141
 
< 0.1%
2.018400064 × 10141
 
< 0.1%
2.01840011 × 10141
 
< 0.1%
2.018400049 × 10141
 
< 0.1%
2.018400078 × 10141
 
< 0.1%
Other values (648312)648312
> 99.9%
ValueCountFrequency (%)
2.012400076 × 10141
< 0.1%
2.012400076 × 10141
< 0.1%
2.012400076 × 10141
< 0.1%
2.012400076 × 10141
< 0.1%
2.012400076 × 10141
< 0.1%
2.012400076 × 10141
< 0.1%
2.012400076 × 10141
< 0.1%
2.012400076 × 10141
< 0.1%
2.012400076 × 10141
< 0.1%
2.012400076 × 10141
< 0.1%
ValueCountFrequency (%)
2.020400135 × 10141
< 0.1%
2.020400135 × 10141
< 0.1%
2.020400135 × 10141
< 0.1%
2.020400135 × 10141
< 0.1%
2.020400135 × 10141
< 0.1%
2.020400135 × 10141
< 0.1%
2.020400135 × 10141
< 0.1%
2.020400135 × 10141
< 0.1%
2.020400135 × 10141
< 0.1%
2.020400135 × 10141
< 0.1%

ageclass
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.91999346
Minimum0
Maximum105
Zeros6689
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:31.494121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median40
Q355
95-th percentile80
Maximum105
Range105
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.97714858
Coefficient of variation (CV)0.5505298642
Kurtosis-0.7608938002
Mean39.91999346
Median Absolute Deviation (MAD)15
Skewness0.2134019693
Sum25881010
Variance482.9950596
MonotonicityNot monotonic
2022-09-29T09:24:31.584576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2564641
 
10.0%
3062707
 
9.7%
3551958
 
8.0%
5044763
 
6.9%
4043798
 
6.8%
4543163
 
6.7%
2042044
 
6.5%
5540684
 
6.3%
6034933
 
5.4%
6531031
 
4.8%
Other values (20)188600
29.1%
ValueCountFrequency (%)
06689
1.0%
16347
1.0%
25908
0.9%
35610
0.9%
45358
0.8%
55222
0.8%
65115
0.8%
74992
0.8%
84883
0.8%
94818
0.7%
ValueCountFrequency (%)
1053
 
< 0.1%
10063
 
< 0.1%
95801
 
0.1%
904120
 
0.6%
8511261
 
1.7%
8018486
2.9%
7523493
3.6%
7028031
4.3%
6531031
4.8%
6034933
5.4%

nationalityclass
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
Switzerland
495013 
Central Europe
 
44792
Other
 
42671
Southern Europe
 
36095
Southeastern Europe
 
18834
Other values (3)
 
10917

Length

Max length19
Median length11
Mean length11.32123852
Min length5

Characters and Unicode

Total characters7339808
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSwitzerland
2nd rowCentral Europe
3rd rowSoutheastern Europe
4th rowSoutheastern Europe
5th rowSoutheastern Europe

Common Values

ValueCountFrequency (%)
Switzerland495013
76.4%
Central Europe44792
 
6.9%
Other42671
 
6.6%
Southern Europe36095
 
5.6%
Southeastern Europe18834
 
2.9%
Western Europe7039
 
1.1%
Northern Europe2113
 
0.3%
Eastern Europe1765
 
0.3%

Length

2022-09-29T09:24:31.679956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:24:31.781132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
switzerland495013
65.2%
europe110638
 
14.6%
central44792
 
5.9%
other42671
 
5.6%
southern36095
 
4.8%
southeastern18834
 
2.5%
western7039
 
0.9%
northern2113
 
0.3%
eastern1765
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e784833
10.7%
r761073
10.4%
t667156
9.1%
n605651
8.3%
a560404
7.6%
S549942
 
7.5%
l539805
 
7.4%
z495013
 
6.7%
i495013
 
6.7%
d495013
 
6.7%
Other values (12)1385905
18.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6470210
88.2%
Uppercase Letter758960
 
10.3%
Space Separator110638
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e784833
12.1%
r761073
11.8%
t667156
10.3%
n605651
9.4%
a560404
8.7%
l539805
8.3%
z495013
7.7%
i495013
7.7%
d495013
7.7%
w495013
7.7%
Other values (5)571236
8.8%
Uppercase Letter
ValueCountFrequency (%)
S549942
72.5%
E112403
 
14.8%
C44792
 
5.9%
O42671
 
5.6%
W7039
 
0.9%
N2113
 
0.3%
Space Separator
ValueCountFrequency (%)
110638
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7229170
98.5%
Common110638
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e784833
10.9%
r761073
10.5%
t667156
9.2%
n605651
8.4%
a560404
7.8%
S549942
7.6%
l539805
7.5%
z495013
 
6.8%
i495013
 
6.8%
d495013
 
6.8%
Other values (11)1275267
17.6%
Common
ValueCountFrequency (%)
110638
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7339808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e784833
10.7%
r761073
10.4%
t667156
9.1%
n605651
8.3%
a560404
7.6%
S549942
 
7.5%
l539805
 
7.4%
z495013
 
6.7%
i495013
 
6.7%
d495013
 
6.7%
Other values (12)1385905
18.9%

populationtype
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
1
628018 
3
 
16464
2
 
3817
4
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters648322
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1628018
96.9%
316464
 
2.5%
23817
 
0.6%
423
 
< 0.1%

Length

2022-09-29T09:24:31.875781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:24:31.967064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1628018
96.9%
316464
 
2.5%
23817
 
0.6%
423
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1628018
96.9%
316464
 
2.5%
23817
 
0.6%
423
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number648322
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1628018
96.9%
316464
 
2.5%
23817
 
0.6%
423
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common648322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1628018
96.9%
316464
 
2.5%
23817
 
0.6%
423
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII648322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1628018
96.9%
316464
 
2.5%
23817
 
0.6%
423
 
< 0.1%

maritalstatusclass
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
1
331607 
2
233250 
4
54285 
3
 
29150
-9
 
30

Length

Max length2
Median length1
Mean length1.000046273
Min length1

Characters and Unicode

Total characters648352
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1331607
51.1%
2233250
36.0%
454285
 
8.4%
329150
 
4.5%
-930
 
< 0.1%

Length

2022-09-29T09:24:32.040382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:24:32.132246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1331607
51.1%
2233250
36.0%
454285
 
8.4%
329150
 
4.5%
930
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1331607
51.1%
2233250
36.0%
454285
 
8.4%
329150
 
4.5%
-30
 
< 0.1%
930
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number648322
> 99.9%
Dash Punctuation30
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1331607
51.1%
2233250
36.0%
454285
 
8.4%
329150
 
4.5%
930
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common648352
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1331607
51.1%
2233250
36.0%
454285
 
8.4%
329150
 
4.5%
-30
 
< 0.1%
930
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII648352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1331607
51.1%
2233250
36.0%
454285
 
8.4%
329150
 
4.5%
-30
 
< 0.1%
930
 
< 0.1%

arrivalyearmunicipality
Real number (ℝ≥0)

HIGH CORRELATION

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3384.576534
Minimum1922
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:32.228731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1922
5-th percentile1967
Q11997
median2010
Q32017
95-th percentile9997
Maximum9999
Range8077
Interquartile range (IQR)20

Descriptive statistics

Standard deviation3024.973862
Coefficient of variation (CV)0.8937525362
Kurtosis0.9876010888
Mean3384.576534
Median Absolute Deviation (MAD)9
Skewness1.728417163
Sum2194295428
Variance9150466.863
MonotonicityNot monotonic
2022-09-29T09:24:32.339218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9997112147
 
17.3%
201228045
 
4.3%
201426309
 
4.1%
201524163
 
3.7%
201322421
 
3.5%
201122250
 
3.4%
201620976
 
3.2%
201019274
 
3.0%
200919266
 
3.0%
200818516
 
2.9%
Other values (91)334955
51.7%
ValueCountFrequency (%)
19221
 
< 0.1%
19231
 
< 0.1%
19241
 
< 0.1%
19252
 
< 0.1%
19267
 
< 0.1%
192716
< 0.1%
192829
< 0.1%
192916
< 0.1%
193015
 
< 0.1%
193138
< 0.1%
ValueCountFrequency (%)
999949
 
< 0.1%
9997112147
17.3%
20206576
 
1.0%
201912515
 
1.9%
201815700
 
2.4%
201718233
 
2.8%
201620976
 
3.2%
201524163
 
3.7%
201426309
 
4.1%
201322421
 
3.5%

arrivalyearswitzerland
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct84
Distinct (%)< 0.1%
Missing309786
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean1043.882893
Minimum-5
Maximum2020
Zeros0
Zeros (%)0.0%
Negative161570
Negative (%)24.9%
Memory size4.9 MiB
2022-09-29T09:24:32.467964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile-5
Q1-5
median1968
Q32007
95-th percentile2016
Maximum2020
Range2025
Interquartile range (IQR)2012

Descriptive statistics

Standard deviation1002.27575
Coefficient of variation (CV)0.9601419435
Kurtosis-1.991344207
Mean1043.882893
Median Absolute Deviation (MAD)50
Skewness-0.09071918136
Sum353391939
Variance1004556.678
MonotonicityNot monotonic
2022-09-29T09:24:32.584286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5161570
24.9%
20128931
 
1.4%
20148645
 
1.3%
20158406
 
1.3%
20117990
 
1.2%
20137952
 
1.2%
20087069
 
1.1%
20106369
 
1.0%
20166352
 
1.0%
20076313
 
1.0%
Other values (74)108939
 
16.8%
(Missing)309786
47.8%
ValueCountFrequency (%)
-5161570
24.9%
19318
 
< 0.1%
19322
 
< 0.1%
193817
 
< 0.1%
19394
 
< 0.1%
19406
 
< 0.1%
19413
 
< 0.1%
19422
 
< 0.1%
19448
 
< 0.1%
19465
 
< 0.1%
ValueCountFrequency (%)
20201401
 
0.2%
20193079
 
0.5%
20184034
0.6%
20175052
0.8%
20166352
1.0%
20158406
1.3%
20148645
1.3%
20137952
1.2%
20128931
1.4%
20117990
1.2%

egid
Categorical

HIGH CARDINALITY

Distinct7701
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
5xpzYwWo6Vs4qUHpPbcHCyb2bXNzuV1ke6eTHb6RVuk=
 
1760
msXpQv+xlQ4cpSL1lBciTtiudZN0nTyKSPNK/a2Cpgo=
 
1745
spwam3nBr088N0FSc+5UQQXld9T4LVTtcDF0/2epOVI=
 
1063
qBiDXzkp79Jv7UoMWT5atdmDoCRdeYkyB9r52VTfchI=
 
961
4o/uHzHZVybdJFOuehm+EG0q/AUC2buECmFsDJLYUqY=
 
885
Other values (7696)
641908 

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters28526168
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowM9+DpAhjwnQRpDeeMxtGpPzNcu3BmCRYu+f6PPDpcFw=
2nd rowM9+DpAhjwnQRpDeeMxtGpPzNcu3BmCRYu+f6PPDpcFw=
3rd row5ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=
4th row5ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=
5th row5ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=

Common Values

ValueCountFrequency (%)
5xpzYwWo6Vs4qUHpPbcHCyb2bXNzuV1ke6eTHb6RVuk=1760
 
0.3%
msXpQv+xlQ4cpSL1lBciTtiudZN0nTyKSPNK/a2Cpgo=1745
 
0.3%
spwam3nBr088N0FSc+5UQQXld9T4LVTtcDF0/2epOVI=1063
 
0.2%
qBiDXzkp79Jv7UoMWT5atdmDoCRdeYkyB9r52VTfchI=961
 
0.1%
4o/uHzHZVybdJFOuehm+EG0q/AUC2buECmFsDJLYUqY=885
 
0.1%
xTw2TUMDOCWvBlZFKTb8Py6pLTVY078AvB9juXAYc4o=851
 
0.1%
tlqIJPBcflgWlvFrG3d4Le4Tx180MdDZu35xB8qbXko=803
 
0.1%
JVeVISGfnsbbin/lNFHivcegGPSXZ3sCcRg5SJCqAtg=798
 
0.1%
UAgfgEOFlz0LaIvvhA8aeBUTsZo8DRe2vbzTD4VMdy0=789
 
0.1%
mHgz4OqC95vIpWu8ha556jGuJ9+RjvMUNK3CpC7FXVw=776
 
0.1%
Other values (7691)637891
98.4%

Length

2022-09-29T09:24:32.687053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5xpzywwo6vs4quhppbchcyb2bxnzuv1ke6ethb6rvuk1760
 
0.3%
msxpqv+xlq4cpsl1lbcittiudzn0ntykspnk/a2cpgo1745
 
0.3%
spwam3nbr088n0fsc+5uqqxld9t4lvttcdf0/2epovi1063
 
0.2%
qbidxzkp79jv7uomwt5atdmdocrdeykyb9r52vtfchi961
 
0.1%
4o/uhzhzvybdjfouehm+eg0q/auc2buecmfsdjlyuqy885
 
0.1%
xtw2tumdocwvblzfktb8py6pltvy078avb9juxayc4o851
 
0.1%
tlqijpbcflgwlvfrg3d4le4tx180mddzu35xb8qbxko803
 
0.1%
jvevisgfnsbbin/lnfhivceggpsxz3sccrg5sjcqatg798
 
0.1%
uagfgeoflz0laivvha8aebutszo8dre2vbztd4vmdy0789
 
0.1%
mhgz4oqc95vipwu8ha556jguj9+rjvmunk3cpc7fxvw776
 
0.1%
Other values (7691)637891
98.4%

Most occurring characters

ValueCountFrequency (%)
=648322
 
2.3%
k484634
 
1.7%
o484209
 
1.7%
c478990
 
1.7%
M471076
 
1.7%
8470683
 
1.7%
Y469337
 
1.6%
g468794
 
1.6%
A467568
 
1.6%
Q466577
 
1.6%
Other values (55)23615978
82.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11332699
39.7%
Uppercase Letter11311461
39.7%
Decimal Number4390350
 
15.4%
Math Symbol1071942
 
3.8%
Other Punctuation419716
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
k484634
 
4.3%
o484209
 
4.3%
c478990
 
4.2%
g468794
 
4.1%
w462744
 
4.1%
s460641
 
4.1%
d439836
 
3.9%
x436840
 
3.9%
p433963
 
3.8%
b431867
 
3.8%
Other values (16)6750181
59.6%
Uppercase Letter
ValueCountFrequency (%)
M471076
 
4.2%
Y469337
 
4.1%
A467568
 
4.1%
Q466577
 
4.1%
I466196
 
4.1%
U460383
 
4.1%
E451199
 
4.0%
B436215
 
3.9%
O431010
 
3.8%
R430905
 
3.8%
Other values (16)6760995
59.8%
Decimal Number
ValueCountFrequency (%)
8470683
10.7%
4462984
10.5%
0461026
10.5%
5437044
10.0%
7433536
9.9%
2429306
9.8%
3427356
9.7%
1423494
9.6%
6423001
9.6%
9421920
9.6%
Math Symbol
ValueCountFrequency (%)
=648322
60.5%
+423620
39.5%
Other Punctuation
ValueCountFrequency (%)
/419716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22644160
79.4%
Common5882008
 
20.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
k484634
 
2.1%
o484209
 
2.1%
c478990
 
2.1%
M471076
 
2.1%
Y469337
 
2.1%
g468794
 
2.1%
A467568
 
2.1%
Q466577
 
2.1%
I466196
 
2.1%
w462744
 
2.0%
Other values (42)17924035
79.2%
Common
ValueCountFrequency (%)
=648322
11.0%
8470683
 
8.0%
4462984
 
7.9%
0461026
 
7.8%
5437044
 
7.4%
7433536
 
7.4%
2429306
 
7.3%
3427356
 
7.3%
+423620
 
7.2%
1423494
 
7.2%
Other values (3)1264637
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII28526168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
=648322
 
2.3%
k484634
 
1.7%
o484209
 
1.7%
c478990
 
1.7%
M471076
 
1.7%
8470683
 
1.7%
Y469337
 
1.6%
g468794
 
1.6%
A467568
 
1.6%
Q466577
 
1.6%
Other values (55)23615978
82.8%

gbaups
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8014.678691
Minimum8011
Maximum8023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:32.760999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8011
5-th percentile8011
Q18012
median8014
Q38016
95-th percentile8022
Maximum8023
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.256659585
Coefficient of variation (CV)0.0004063368864
Kurtosis-0.07925653742
Mean8014.678691
Median Absolute Deviation (MAD)2
Skewness0.9925711294
Sum5196092518
Variance10.60583165
MonotonicityNot monotonic
2022-09-29T09:24:32.847708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
8013119420
18.4%
801497738
15.1%
801296049
14.8%
801186088
13.3%
801576746
11.8%
802229981
 
4.6%
802128988
 
4.5%
801922698
 
3.5%
802022221
 
3.4%
801822218
 
3.4%
Other values (3)46175
 
7.1%
ValueCountFrequency (%)
801186088
13.3%
801296049
14.8%
8013119420
18.4%
801497738
15.1%
801576746
11.8%
801621281
 
3.3%
801718669
 
2.9%
801822218
 
3.4%
801922698
 
3.5%
802022221
 
3.4%
ValueCountFrequency (%)
80236225
 
1.0%
802229981
 
4.6%
802128988
 
4.5%
802022221
 
3.4%
801922698
 
3.5%
801822218
 
3.4%
801718669
 
2.9%
801621281
 
3.3%
801576746
11.8%
801497738
15.1%

gkats
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
1025
487396 
1030
111919 
1021
 
38159
1040
 
10848

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2593288
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1025
2nd row1025
3rd row1025
4th row1025
5th row1025

Common Values

ValueCountFrequency (%)
1025487396
75.2%
1030111919
 
17.3%
102138159
 
5.9%
104010848
 
1.7%

Length

2022-09-29T09:24:32.948015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:24:33.042002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1025487396
75.2%
1030111919
 
17.3%
102138159
 
5.9%
104010848
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0771089
29.7%
1686481
26.5%
2525555
20.3%
5487396
18.8%
3111919
 
4.3%
410848
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2593288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0771089
29.7%
1686481
26.5%
2525555
20.3%
5487396
18.8%
3111919
 
4.3%
410848
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common2593288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0771089
29.7%
1686481
26.5%
2525555
20.3%
5487396
18.8%
3111919
 
4.3%
410848
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2593288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0771089
29.7%
1686481
26.5%
2525555
20.3%
5487396
18.8%
3111919
 
4.3%
410848
 
0.4%

gastws
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.415073066
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:33.129659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q37
95-th percentile9
Maximum31
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.748693059
Coefficient of variation (CV)0.5076003639
Kurtosis29.69035317
Mean5.415073066
Median Absolute Deviation (MAD)1
Skewness3.984673665
Sum3510711
Variance7.55531353
MonotonicityNot monotonic
2022-09-29T09:24:33.212090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4157117
24.2%
5124614
19.2%
687722
13.5%
779013
12.2%
378392
12.1%
846285
 
7.1%
230502
 
4.7%
920682
 
3.2%
105147
 
0.8%
153760
 
0.6%
Other values (7)15088
 
2.3%
ValueCountFrequency (%)
12330
 
0.4%
230502
 
4.7%
378392
12.1%
4157117
24.2%
5124614
19.2%
687722
13.5%
779013
12.2%
846285
 
7.1%
920682
 
3.2%
105147
 
0.8%
ValueCountFrequency (%)
311745
 
0.3%
271760
 
0.3%
161298
 
0.2%
153760
 
0.6%
132413
 
0.4%
122238
 
0.3%
113304
 
0.5%
105147
 
0.8%
920682
3.2%
846285
7.1%

gazwot
Real number (ℝ≥0)

HIGH CORRELATION

Distinct69
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.29786588
Minimum1
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:33.315091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median9
Q314
95-th percentile36
Maximum186
Range185
Interquartile range (IQR)8

Descriptive statistics

Standard deviation17.31660912
Coefficient of variation (CV)1.302209639
Kurtosis45.77917571
Mean13.29786588
Median Absolute Deviation (MAD)4
Skewness5.724694582
Sum8621299
Variance299.8649515
MonotonicityNot monotonic
2022-09-29T09:24:33.427339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
856955
 
8.8%
650585
 
7.8%
144583
 
6.9%
1243187
 
6.7%
741821
 
6.5%
1037651
 
5.8%
434457
 
5.3%
333540
 
5.2%
930832
 
4.8%
1129485
 
4.5%
Other values (59)245226
37.8%
ValueCountFrequency (%)
144583
6.9%
219897
 
3.1%
333540
5.2%
434457
5.3%
520812
 
3.2%
650585
7.8%
741821
6.5%
856955
8.8%
930832
4.8%
1037651
5.8%
ValueCountFrequency (%)
1861745
0.3%
179803
0.1%
1451760
0.3%
105103
 
< 0.1%
89688
 
0.1%
86682
 
0.1%
85695
 
0.1%
83644
 
0.1%
81616
 
0.1%
72702
 
0.1%

eh
Real number (ℝ≥0)

HIGH CORRELATION

Distinct106
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2665736.1
Minimum2658300
Maximum2669800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:33.552666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2658300
5-th percentile2662600
Q12664500
median2665900
Q32666700
95-th percentile2669000
Maximum2669800
Range11500
Interquartile range (IQR)2200

Descriptive statistics

Standard deviation1764.035681
Coefficient of variation (CV)0.0006617443046
Kurtosis-0.005506112717
Mean2665736.1
Median Absolute Deviation (MAD)1000
Skewness-0.1004844608
Sum1.72825536 × 1012
Variance3111821.885
MonotonicityNot monotonic
2022-09-29T09:24:33.668050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266600030873
 
4.8%
266590029953
 
4.6%
266610026315
 
4.1%
266580023428
 
3.6%
266570021074
 
3.3%
266550019512
 
3.0%
266560018510
 
2.9%
266650018132
 
2.8%
266630016700
 
2.6%
266640016700
 
2.6%
Other values (96)427125
65.9%
ValueCountFrequency (%)
265830049
 
< 0.1%
2658500104
< 0.1%
265860032
 
< 0.1%
265890090
< 0.1%
265900051
 
< 0.1%
265910010
 
< 0.1%
265940016
 
< 0.1%
265950081
< 0.1%
2659600145
< 0.1%
265970069
< 0.1%
ValueCountFrequency (%)
26698001539
 
0.2%
26697002675
0.4%
26696001983
 
0.3%
26695002455
 
0.4%
26694003587
0.6%
26693005979
0.9%
26692005550
0.9%
26691006337
1.0%
26690006591
1.0%
26689005998
0.9%

nh
Real number (ℝ≥0)

HIGH CORRELATION

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1211418.419
Minimum1208800
Maximum1214400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:33.783086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1208800
5-th percentile1209700
Q11210600
median1211600
Q31212200
95-th percentile1213000
Maximum1214400
Range5600
Interquartile range (IQR)1600

Descriptive statistics

Standard deviation999.7456894
Coefficient of variation (CV)0.0008252686882
Kurtosis-0.897598571
Mean1211418.419
Median Absolute Deviation (MAD)800
Skewness-0.1470860595
Sum7.853892122 × 1011
Variance999491.4435
MonotonicityNot monotonic
2022-09-29T09:24:33.889533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121160030228
 
4.7%
121200028262
 
4.4%
121180028019
 
4.3%
121170025163
 
3.9%
121190024259
 
3.7%
121210023056
 
3.6%
121240022272
 
3.4%
121250021188
 
3.3%
121130020316
 
3.1%
121230020120
 
3.1%
Other values (44)405439
62.5%
ValueCountFrequency (%)
12088007
 
< 0.1%
120920016
 
< 0.1%
12093003686
 
0.6%
12094002815
 
0.4%
12095005115
 
0.8%
12096009486
1.5%
120970012465
1.9%
120980011368
1.8%
120990015225
2.3%
121000015449
2.4%
ValueCountFrequency (%)
121440037
 
< 0.1%
121430057
 
< 0.1%
121420092
 
< 0.1%
121410016
 
< 0.1%
121400072
 
< 0.1%
121390040
 
< 0.1%
121380051
 
< 0.1%
1213700202
 
< 0.1%
1213600306
 
< 0.1%
12135001291
0.2%

ewid
Real number (ℝ≥0)

HIGH CORRELATION

Distinct186
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.784693409
Minimum1
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:34.012477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q39
95-th percentile24
Maximum186
Range185
Interquartile range (IQR)7

Descriptive statistics

Standard deviation11.61896443
Coefficient of variation (CV)1.49253976
Kurtosis59.6049167
Mean7.784693409
Median Absolute Deviation (MAD)3
Skewness6.267501926
Sum5046988
Variance135.0003344
MonotonicityNot monotonic
2022-09-29T09:24:34.120234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1113790
17.6%
276111
11.7%
366539
10.3%
456140
8.7%
548451
 
7.5%
644444
 
6.9%
735839
 
5.5%
830824
 
4.8%
924708
 
3.8%
1020722
 
3.2%
Other values (176)130754
20.2%
ValueCountFrequency (%)
1113790
17.6%
276111
11.7%
366539
10.3%
456140
8.7%
548451
7.5%
644444
 
6.9%
735839
 
5.5%
830824
 
4.8%
924708
 
3.8%
1020722
 
3.2%
ValueCountFrequency (%)
18610
< 0.1%
18513
< 0.1%
18415
< 0.1%
1837
 
< 0.1%
1828
< 0.1%
1817
 
< 0.1%
1808
< 0.1%
1798
< 0.1%
1785
 
< 0.1%
17719
< 0.1%

wazimclass
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
3-4
428701 
5-6
117498 
1-2
86693 
>6
 
15430

Length

Max length3
Median length3
Mean length2.976200098
Min length2

Characters and Unicode

Total characters1929536
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3-4
2nd row3-4
3rd row3-4
4th row3-4
5th row3-4

Common Values

ValueCountFrequency (%)
3-4428701
66.1%
5-6117498
 
18.1%
1-286693
 
13.4%
>615430
 
2.4%

Length

2022-09-29T09:24:34.222992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:24:34.313368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3-4428701
66.1%
5-6117498
 
18.1%
1-286693
 
13.4%
615430
 
2.4%

Most occurring characters

ValueCountFrequency (%)
-632892
32.8%
3428701
22.2%
4428701
22.2%
6132928
 
6.9%
5117498
 
6.1%
186693
 
4.5%
286693
 
4.5%
>15430
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1281214
66.4%
Dash Punctuation632892
32.8%
Math Symbol15430
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3428701
33.5%
4428701
33.5%
6132928
 
10.4%
5117498
 
9.2%
186693
 
6.8%
286693
 
6.8%
Dash Punctuation
ValueCountFrequency (%)
-632892
100.0%
Math Symbol
ValueCountFrequency (%)
>15430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1929536
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-632892
32.8%
3428701
22.2%
4428701
22.2%
6132928
 
6.9%
5117498
 
6.1%
186693
 
4.5%
286693
 
4.5%
>15430
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1929536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-632892
32.8%
3428701
22.2%
4428701
22.2%
6132928
 
6.9%
5117498
 
6.1%
186693
 
4.5%
286693
 
4.5%
>15430
 
0.8%

wareaclass
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
70-99
245531 
100-149
202540 
50-69
87381 
>150
65659 
<50
47211 

Length

Max length7
Median length5
Mean length5.377897094
Min length3

Characters and Unicode

Total characters3486609
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row70-99
2nd row70-99
3rd row100-149
4th row100-149
5th row100-149

Common Values

ValueCountFrequency (%)
70-99245531
37.9%
100-149202540
31.2%
50-6987381
 
13.5%
>15065659
 
10.1%
<5047211
 
7.3%

Length

2022-09-29T09:24:34.405372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:24:34.511456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
70-99245531
37.9%
100-149202540
31.2%
50-6987381
 
13.5%
15065659
 
10.1%
5047211
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0850862
24.4%
9780983
22.4%
-535452
15.4%
1470739
13.5%
7245531
 
7.0%
4202540
 
5.8%
5200251
 
5.7%
687381
 
2.5%
>65659
 
1.9%
<47211
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2838287
81.4%
Dash Punctuation535452
 
15.4%
Math Symbol112870
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0850862
30.0%
9780983
27.5%
1470739
16.6%
7245531
 
8.7%
4202540
 
7.1%
5200251
 
7.1%
687381
 
3.1%
Math Symbol
ValueCountFrequency (%)
>65659
58.2%
<47211
41.8%
Dash Punctuation
ValueCountFrequency (%)
-535452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3486609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0850862
24.4%
9780983
22.4%
-535452
15.4%
1470739
13.5%
7245531
 
7.0%
4202540
 
5.8%
5200251
 
5.7%
687381
 
2.5%
>65659
 
1.9%
<47211
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3486609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0850862
24.4%
9780983
22.4%
-535452
15.4%
1470739
13.5%
7245531
 
7.0%
4202540
 
5.8%
5200251
 
5.7%
687381
 
2.5%
>65659
 
1.9%
<47211
 
1.4%

householdid
Real number (ℝ≥0)

HIGH CORRELATION

Distinct333792
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.016500895 × 1014
Minimum2.0121061 × 1014
Maximum2.020106138 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-09-29T09:24:34.618052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.0121061 × 1014
5-th percentile2.0121061 × 1014
Q12.0151061 × 1014
median2.017106112 × 1014
Q32.019106112 × 1014
95-th percentile2.020106121 × 1014
Maximum2.020106138 × 1014
Range8.000038074 × 1011
Interquartile range (IQR)4.000012078 × 1011

Descriptive statistics

Standard deviation2.491507909 × 1011
Coefficient of variation (CV)0.001235560032
Kurtosis-0.9710262213
Mean2.016500895 × 1014
Median Absolute Deviation (MAD)2.000011704 × 1011
Skewness-0.2411128973
Sum1.606980786 × 1018
Variance6.207611663 × 1022
MonotonicityNot monotonic
2022-09-29T09:24:34.743529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.019106118 × 101413
 
< 0.1%
2.0141061 × 101412
 
< 0.1%
2.01910612 × 101412
 
< 0.1%
2.0121061 × 101412
 
< 0.1%
2.020106116 × 101411
 
< 0.1%
2.02010612 × 101411
 
< 0.1%
2.0141061 × 101411
 
< 0.1%
2.0151061 × 101411
 
< 0.1%
2.016106121 × 101411
 
< 0.1%
2.0151061 × 101411
 
< 0.1%
Other values (333782)648207
> 99.9%
ValueCountFrequency (%)
2.0121061 × 10142
< 0.1%
2.0121061 × 10144
< 0.1%
2.0121061 × 10143
< 0.1%
2.0121061 × 10143
< 0.1%
2.0121061 × 10141
 
< 0.1%
2.0121061 × 10143
< 0.1%
2.0121061 × 10142
< 0.1%
2.0121061 × 10143
< 0.1%
2.0121061 × 10142
< 0.1%
2.0121061 × 10144
< 0.1%
ValueCountFrequency (%)
2.020106138 × 10141
 
< 0.1%
2.020106138 × 10142
 
< 0.1%
2.020106138 × 10142
 
< 0.1%
2.020106138 × 10141
 
< 0.1%
2.020106138 × 10142
 
< 0.1%
2.020106138 × 10142
 
< 0.1%
2.020106138 × 10141
 
< 0.1%
2.020106138 × 10143
< 0.1%
2.020106138 × 10141
 
< 0.1%
2.020106138 × 10145
< 0.1%

Interactions

2022-09-29T09:24:24.603274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:56.350323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:59.027373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:01.374638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:03.763386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:06.485921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:08.517334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:11.390771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:14.085418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:16.679935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:19.275910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:21.818243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:24.807863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:56.552971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:59.246442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:01.576062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:03.984350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:06.679938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:09.068144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:11.627810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:14.308012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:16.893435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:19.490680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:22.028688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:25.035292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:56.763026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:59.456763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:01.787250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:04.206583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:06.838783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:09.300642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:11.862237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:14.560485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:17.106945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:19.720033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:22.237806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:25.236243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:56.960611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:59.644337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:01.990597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:04.475945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:06.982743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:09.519526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:12.143255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:14.790712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:17.321818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:19.940802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:22.440247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:25.404577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:57.124849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:59.800453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:02.156728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:04.713541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:07.132013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:09.683613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:12.342082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:14.960534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:17.505086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:20.104843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:22.603194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:25.628202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:57.345540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:59.992244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:02.370630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:05.008003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:07.304797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:09.887959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:12.552477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:15.177529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:17.714937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:20.334798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:22.822905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:25.854664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:57.564277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:00.187988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:02.565757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:05.234129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:07.459685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:10.092782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:12.755359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:15.389730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:17.949620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:20.552671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:23.031827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:26.086005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:58.016095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:00.377995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:02.769766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:05.435844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:07.604552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:10.337432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:13.008090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:15.593119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:18.151702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:20.775520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:23.252161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:26.318528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:58.219074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:00.568329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:02.972069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:05.632729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:07.784792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:10.554491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:13.232565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:15.809914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:18.357438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:21.009518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:23.467334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:26.525207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:58.409833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:00.767097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:03.175179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:05.881946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:07.940672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:10.766328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:13.445628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:16.019216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:18.577321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:21.212167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:23.973841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:26.719813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:58.619661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:00.957476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:03.369688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:06.103449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:08.086219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:10.974506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:13.654106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:16.237062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:18.823861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:21.414629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:24.171843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:26.923163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:23:58.822325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:01.153866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:03.567657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:06.312657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:08.283632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:11.173695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:13.862158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:16.464148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:19.052828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:21.611218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:24:24.381341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-29T09:24:34.862662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-29T09:24:35.041473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-29T09:24:35.223541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-29T09:24:35.389439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-29T09:24:35.528994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-29T09:24:27.355101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-29T09:24:28.374265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-29T09:24:30.236524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

reportingmunicipalityidstatyearpersonpseudoidageclassnationalityclasspopulationtypemaritalstatusclassarrivalyearmunicipalityarrivalyearswitzerlandegidgbaupsgkatsgastwsgazwotehnhewidwazimclasswareaclasshouseholdid
01061201220124001527638130Switzerland119997-5.0M9+DpAhjwnQRpDeeMxtGpPzNcu3BmCRYu+f6PPDpcFw=8014102511432664100121190033-470-99201210610007544
11061201220124000839461760Central Europe1219781978.0M9+DpAhjwnQRpDeeMxtGpPzNcu3BmCRYu+f6PPDpcFw=8014102511432664100121190033-470-99201210610007544
21061201220124000853074865Southeastern Europe1219971992.05ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=801110258272664300121200023-4100-149201210610007311
31061201220124000783807410Southeastern Europe119997-5.05ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=801110258272664300121200023-4100-149201210610007311
41061201220124000919628915Southeastern Europe119997-5.05ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=801110258272664300121200023-4100-149201210610007311
51061201220124001569326435Southeastern Europe1219971992.05ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=801110258272664300121200023-4100-149201210610007311
61061201220124001266554710Southeastern Europe119997-5.05ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=801110258272664300121200023-4100-149201210610007311
71061201220124000972717860Southeastern Europe1219971990.05ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=801110258272664300121200023-4100-149201210610007311
81061201220124000965192335Southeastern Europe1219971991.05ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=801110258272664300121200023-4100-149201210610007311
9106120122012400124352397Switzerland119997-5.05ksXQiLBDK0QW/BfKkShbFkU6VpevtuxJnkCr9PImAc=8011102582726643001212000163-4100-149201210610007308

Last rows

reportingmunicipalityidstatyearpersonpseudoidageclassnationalityclasspopulationtypemaritalstatusclassarrivalyearmunicipalityarrivalyearswitzerlandegidgbaupsgkatsgastwsgazwotehnhewidwazimclasswareaclasshouseholdid
6483121061202020204000878772740Switzerland122010NaNmXJLPJGSb0tpUTG9WaASo02TE6pNfUgPctdBRxmrgfQ=80201025372667100121010043-4100-149202010611192378
6483131061202020204001266204760Other1220011986.039dfXfX6JFTCHXqDfbowd1vZr1otQOBy43ZJP4KFYOQ=80141025582662700121140073-470-99202010611986397
648314106120202020400101783191Southern Europe119997-5.0mHgz4OqC95vIpWu8ha556jGuJ9+RjvMUNK3CpC7FXVw=8014102573626622001211100113-470-99202010611985166
6483151061202020204000466284430Southern Europe1220152015.0mHgz4OqC95vIpWu8ha556jGuJ9+RjvMUNK3CpC7FXVw=8014102573626622001211100113-470-99202010611985166
6483161061202020204000960593660Switzerland121983NaN+v7aZSHghkrwavngiP7YtMCM4SVdcXTVnibQwVqDW0w=8021102571626666001210700173-4100-149202010611209123
6483171061202020204000554775120Switzerland119997-5.0+v7aZSHghkrwavngiP7YtMCM4SVdcXTVnibQwVqDW0w=8021102571626666001210700173-4100-149202010611209123
6483181061202020204000469279560Switzerland121966NaN+v7aZSHghkrwavngiP7YtMCM4SVdcXTVnibQwVqDW0w=8021102571626666001210700173-4100-149202010611209123
648319106120202020400042073597Switzerland119997-5.0mXJLPJGSb0tpUTG9WaASo02TE6pNfUgPctdBRxmrgfQ=80201025372667100121010043-4100-149202010611192378
6483201061202020204000604550940Switzerland1220022002.0+v7aZSHghkrwavngiP7YtMCM4SVdcXTVnibQwVqDW0w=802110257162666600121070023-470-99202010611209124
6483211061202020204000705365750Switzerland1219901990.0+v7aZSHghkrwavngiP7YtMCM4SVdcXTVnibQwVqDW0w=802110257162666600121070023-470-99202010611209124